{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "622fac5c",
   "metadata": {},
   "source": [
    "# **<font size=4 color=#BB3D00 face=微软雅黑>创建均匀和非均匀时间向量</font>**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "59e91d66",
   "metadata": {},
   "source": [
    "您可以创建均匀和不均匀时间向量以用于涉及时间序列的计算。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da7a31bd",
   "metadata": {},
   "source": [
    "## <font size=3  face=微软雅黑>**※Matlab案例**</font> "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd5263aa",
   "metadata": {},
   "source": [
    "网址：https://ww2.mathworks.cn/help/signal/ug/create-uniform-and-nonuniform-time-vectors.html    \n",
    "描述：本案例由2个示例构成。\n",
    "### - <font size=3 color=DarkOrChid size=3>示例1：创建均匀时间向量</font>\n",
    "### - <font size=3 color=DarkOrChid size=3>示例2：创建非均匀时间向量</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "883d8514",
   "metadata": {},
   "source": [
    "## <font size=3 face=微软雅黑>**※Python案例**</font> "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f07a36b",
   "metadata": {},
   "source": [
    "针对以上案例，采用Python语言实现。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "607386f8",
   "metadata": {},
   "source": [
    "### - <font color=DarkOrChid size=3>示例1：创建均匀时间向量</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "774b0f1a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#libraries\n",
    "import numpy as np\n",
    "from scipy.io import loadmat\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a27947ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "gpl1 = loadmat(\"gpl1.mat\")\n",
    "gpl2 = loadmat(\"gpl2.mat\")\n",
    "Tcont = np.linspace(0,3,20001)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c4ee9ce",
   "metadata": {},
   "source": [
    "### - <font color=DarkOrChid size=3>示例2：创建非均匀时间向量</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "552cea4c",
   "metadata": {},
   "outputs": [],
   "source": [
    "Ffast = 100\n",
    "Tf = 1/Ffast\n",
    "Nslow = 5\n",
    "#tdisc = [linspace(0,1,Nslow) 1+Tf:Tf:2-Tf linspace(2,3,Nslow)]\n",
    "tdisc = np.linspace(0,1,Nslow)\n",
    "#Tdisc = np.array(tdisc)\n",
    "Nfast = (2-1)/Tf\n",
    "#print(Nfast)\n",
    "tdisc1 = np.linspace(1,2,99)\n",
    "tdisc = np.append(tdisc,tdisc1)\n",
    "tdisc2 = np.linspace(2,3,Nslow)\n",
    "tdisc = np.append(tdisc,tdisc2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f666659e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Amplitude')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(tdisc,gpl2['gpl2'].flatten(),'o',ms = 5,mfc='w',mew=1,mec='b')\n",
    "plt.plot(Tcont,gpl1['gpl1'])\n",
    "plt.title('Triangular_pulse Wave')\n",
    "plt.xlabel('Time (sec)')\n",
    "plt.ylabel('Amplitude')\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
